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Background: Seed morphology and color are critical agronomic traits in Medicago spp., reflecting adaptations to diverse environments and influencing seedling establishment and vigor. Understanding the interplay between seed traits, geographic origin, and genetic diversity is crucial for effective germplasm conservation and breeding. This study presents a comprehensive analysis of these factors in a diverse collection of Medicago accessions, leveraging machine learning to illuminate these complex relationships. Results: We analyzed seed size, shape, and color data from 318 Medicago accessions representing 29 species/subspecies from 31 countries. Machine learning models, including Neural Boost, Bootstrap Forest, and Support Vector Machines, effectively classified accessions based on seed traits and geographic origin, achieving up to 80% accuracy. Seed size was accurately predicted (R-squared > 0.80) using a combination of species, geographic origin, and shape descriptors. Hierarchical clustering of 189 M. sativa accessions based on 8,565 SNP markers revealed 20 distinct genetic clusters, indicating substantial population structure. A machine learning-based genome-wide association (GWA) analysis identified SNPs on chromosomes 1, 6, and 8 with high importance for predicting geographic origin. Notably, the most significant SNPs were located in or near genes involved in stress response and genome stability, suggesting their potential role in local adaptation. Finally, we successfully imputed missing M. sativa SNP genotypes using multiple machine learning approaches, achieving over 70% accuracy overall and over 80% for individual nucleotides (A, T, C, G), enhancing the utility of genomic datasets with missing data. Conclusions: Our integrated analysis of phenotypic, genetic, and geographic data, coupled with a machine learning-based GWAS approach, provides valuable insights into the diverse patterns within Medicago spp. We demonstrate the power of machine learning for germplasm characterization, trait prediction, and imputation of missing genomic data. These findings have significant implications for seed trait improvement, germplasm management, and understanding adaptation in Medicago and other diverse crop species. The identified candidate genes associated with geographic origin provide a foundation for future investigations into the functional mechanisms of local adaptation. Furthermore, our imputation method offers a valuable data for maximizing the utility of genomic resources in Medicago and other species. [ABSTRACT FROM AUTHOR] more...

Coffea stenophylla is a rare Coffea species boasting a flavor profile comparable to Arabica coffee (Coffea arabica) and has a good adaptability to lowland tropical climates. This species faces increasing threats from climate change, deforestation, and habitat fragmentation in its West African homeland. Using 1037 novel SNP markers derived from Genotyping-by-Sequencing (GBS), we revealed the presence of three distinct natural populations (mean Fst = 0.176) in Sierra Leone. Evidence of recent bottlenecks and small effective population size (118–140) was found across all three populations, reflecting the impact of recent anthropogenic disturbances on this species. Using a model-flexible inference approach, we unveiled a strong ancient bottleneck approximately 23,000 years ago, coinciding with the last glacial maximum (LGM), followed by post-glacial expansion and divergence into distinct genetic clusters. A comparative analysis between ex situ genebanks and natural populations detected a significant gap in genetic diversity, with two out of three natural populations missing from the ex situ genebank collection. These findings highlight the urgent need to improve conservation practices for C. stenophylla in Sierra Leone. The novel SNP markers developed in this study provided valuable tools to support future efforts in conservation and utilization of C. stenophylla genetic resources in West Africa. [ABSTRACT FROM AUTHOR] more...

Arbuscular mycorrhizal fungi (AMF, Glomeromycotina), in addition to forming symbioses with the majority of land plants, harbor vertically transmitted endosymbiotic bacteria 'Candidatus Glomeribacter gigasporarum' (CaGg) and 'Candidatus Moeniiplasma glomeromycotorum' (CaMg). CaGg is a nonessential mutualist of AMF, whereas the lifestyle of CaMg is unknown. To start unraveling the interactions between AMF and their endosymbionts in nature, we examined diversity and distribution of AMF-associated endobacteria in North Atlantic dunes at Cape Cod. Of nearly 500 foredune AMF isolates successfully genotyped during a systematic study, 94% were classified as Gigasporaceae. Two percent of all AMF spores harbored CaGg, and 88% contained CaMg. CaGg was found only in the Gigasporaceae, whereas CaMg was present in Gigasporaceae, Acaulosporaceae, and Diversisporaceae. Incidence of CaGg across AMF was not affected by any of the environmental parameters measured, whereas distribution of CaMg in one of the fungal hosts was impacted by plant density. CaMg populations associated with AMF individuals displayed high levels of genetic diversity but no evidence of gene flow, suggesting that host physical proximity is not sufficient to facilitate horizontal transmission of CaMg. Finally, in addition to a novel lineage of CaGg, we discovered that AMF likely harbor Burkholderia-related bacteria with close phylogenetic affinity to free-living Burkholderia and endobacteria of other Mucoromycota fungi. more...

Anthracnose and grain mold are two of the most significant diseases of sorghum, a versatile crop that plays an important part in the daily lives of millions of inhabitants, especially in the drier tropical regions. The aim of this study was to determine the influence of four agronomic traits in selected sorghum germplasms on the two diseases using Spearman's ρ test to identify significant pairwise correlations. Both anthracnose and grain mold scores were significantly and negatively correlated with seed weight and germination rate. The grain mold infection score also demonstrated negative correlations with plant height (Spearman ρ = −0.61 and p-value = <0.0001) and panicle length (Spearman ρ = −0.27 and p-value = 0.0022). In this investigation, principal component analysis and clustering variables analysis revealed that seed weight and germination rate exhibited a directional alignment, suggesting a positive association. Similarly, panicle length and plant height clustered together, suggesting a shared variation pattern. Additionally, a support vector machine and random forest models effectively predicted the germination rate based on the studied traits, highlighting the potential of machine learning in understanding complex trait relationships in sorghum. This work provides insights into the relationship between agronomic traits and disease resistance, thus contributing to sorghum improvement efforts. [ABSTRACT FROM AUTHOR] more...

A newly documented pathotype 5 of the soil-borne fungus Sporisorium reilianum, causing head smut in sorghum, was tested against 153 unexplored Senegalese sorghum accessions. Among the 153 sorghum accessions tested, 63 (41%) exhibited complete resistance, showing no signs of infection by the fungus. The remaining 90 accessions (59%) displayed varying degrees of susceptibility. Sorghum responses against S. reilianum were explored to analyze the potential link with previously known seed morphology-related traits and new phenotype data from 59 lines for seed weight. A genome-wide association study (GWAS) screened 297,876 SNPs and identified highly significant associations (p < 1 × 10−5) with head smut resistance in sorghum. By mapping these significant SNPs to the reference genome, this study revealed 35 novel candidate defense genes potentially involved in disease resistance. [ABSTRACT FROM AUTHOR] more...

Anthracnose, incited by Colletotrichum sublineola, is the most destructive foliar disease of sorghum and, under severe conditions, yield losses can exceed 80% on susceptible cultivars. The hyper-variable nature of the pathogen makes its management challenging despite the occurrence of several resistant sources. In this study, the genetic variability and pathogenicity of 140 isolates of C. sublineola, which were sequenced using restriction site-associated sequencing (RAD-Seq), resulted in 1244 quality SNPs. The genetic relationship based on the SNP data showed low to high genetic diversity based on isolates' origin. Isolates from Georgia and North Carolina were grouped into multiple clusters with some level of genetic relationships to each other. Even though some isolates from Texas formed a cluster, others clustered with isolates from Puerto Rico. The isolates from Puerto Rico showed scattered distribution, indicating the diverse nature of these isolates. A population structure and cluster analysis revealed that the genetic variation was stratified into eight populations and one admixture group. The virulence pattern of 30 sequenced isolates on 18 sorghum differential lines revealed 27 new pathotypes. SC748-5, SC112-14, and Brandes were resistant to all the tested isolates, while BTx623 was susceptible to all. Line TAM428 was susceptible to all the pathotypes, except for pathotype 26. Future use of the 18 differentials employed in this study, which contains cultivars/lines which have been used in the Americas, Asia, and Africa, could allow for better characterization of C. sublineola pathotypes at a global level, thus accelerating the development of sorghum lines with stable resistance to the anthracnose pathogen. [ABSTRACT FROM AUTHOR] more...

Coffea stenophylla is a rare Coffea species boasting a flavor profile comparable to Arabica coffee ( Coffea arabica ) and has a good adaptability to lowland tropical climates. This species faces increasing threats from climate change, deforestation, and habitat fragmentation in its West African homeland. Using 1037 novel SNP markers derived from Genotyping-by-Sequencing (GBS), we revealed the presence of three distinct natural populations (mean Fst = 0.176) in Sierra Leone. Evidence of recent bottlenecks and small effective population size (118-140) was found across all three populations, reflecting the impact of recent anthropogenic disturbances on this species. Using a model-flexible inference approach, we unveiled a strong ancient bottleneck approximately 23,000 years ago, coinciding with the last glacial maximum (LGM), followed by post-glacial expansion and divergence into distinct genetic clusters. A comparative analysis between ex situ genebanks and natural populations detected a significant gap in genetic diversity, with two out of three natural populations missing from the ex situ genebank collection. These findings highlight the urgent need to improve conservation practices for C. stenophylla in Sierra Leone. The novel SNP markers developed in this study provided valuable tools to support future efforts in conservation and utilization of C. stenophylla genetic resources in West Africa. more...